import pandas as pd
import numpy as np
import matplotlib.pyplot  as plt
import seaborn as sns
import warnings
import plotly.express as px

df = pd.read_excel("C:/Users/vamsi alluri/PROJECTS/BANK LOAN ANALYSIS/financial_loan.xlsx")

df.head()

df.tail()

print("No of Rows :", df.shape[0])

No of Rows : 38576

print("No of Columns :", df.shape[1])

No of Columns : 24

df.info

<bound method DataFrame.info of             id address_state application_type emp_length  \
0      1077430            GA       INDIVIDUAL   < 1 year   
1      1072053            CA       INDIVIDUAL    9 years   
2      1069243            CA       INDIVIDUAL    4 years   
3      1041756            TX       INDIVIDUAL   < 1 year   
4      1068350            IL       INDIVIDUAL  10+ years   
...        ...           ...              ...        ...   
38571   803452            NJ       INDIVIDUAL   < 1 year   
38572   970377            NY       INDIVIDUAL    8 years   
38573   875376            CA       INDIVIDUAL    5 years   
38574   972997            NY       INDIVIDUAL    5 years   
38575   682952            NY       INDIVIDUAL    4 years   

                             emp_title grade home_ownership issue_date  \
0                                Ryder     C           RENT 2021-02-11   
1                       MKC Accounting     E           RENT 2021-01-01   
2                Chemat Technology Inc     C           RENT 2021-01-05   
3                  barnes distribution     B       MORTGAGE 2021-02-25   
4                        J&J Steel Inc     A       MORTGAGE 2021-01-01   
...                                ...   ...            ...        ...   
38571         Joseph M Sanzari Company     C       MORTGAGE 2021-07-11   
38572                        Swat Fame     C           RENT 2021-10-11   
38573  Anaheim Regional Medical Center     D           RENT 2021-09-11   
38574               Brooklyn Radiology     D           RENT 2021-10-11   
38575                    Allen Edmonds     F           RENT 2021-07-11   

      last_credit_pull_date last_payment_date  ... sub_grade        term  \
0                2021-09-13        2021-04-13  ...        C4   60 months   
1                2021-12-14        2021-01-15  ...        E1   36 months   
2                2021-12-12        2021-01-09  ...        C5   36 months   
3                2021-12-12        2021-03-12  ...        B2   60 months   
4                2021-12-14        2021-01-15  ...        A1   36 months   
...                     ...               ...  ...       ...         ...   
38571            2021-05-16        2021-05-16  ...        C1   60 months   
38572            2021-04-16        2021-05-16  ...        C1   60 months   
38573            2021-05-16        2021-05-16  ...        D5   60 months   
38574            2021-05-16        2021-05-16  ...        D5   60 months   
38575            2021-05-16        2021-05-16  ...        F3   60 months   

       verification_status annual_income     dti installment int_rate  \
0          Source Verified       30000.0  0.0100       59.83   0.1527   
1          Source Verified       48000.0  0.0535      109.43   0.1864   
2             Not Verified       50000.0  0.2088      421.65   0.1596   
3          Source Verified       42000.0  0.0540       97.06   0.1065   
4                 Verified       83000.0  0.0231      106.53   0.0603   
...                    ...           ...     ...         ...      ...   
38571             Verified      100000.0  0.1986      551.64   0.1299   
38572             Verified       50000.0  0.0458      579.72   0.1349   
38573             Verified       65000.0  0.1734      627.93   0.1749   
38574             Verified      368000.0  0.0009      612.72   0.1825   
38575             Verified       80000.0  0.0600      486.86   0.2099   

       loan_amount  total_acc  total_payment  
0             2500          4           1009  
1             3000          4           3939  
2            12000         11           3522  
3             4500          9           4911  
4             3500         28           3835  
...            ...        ...            ...  
38571        24250         33          31946  
38572        25200         18          31870  
38573        25000         20          35721  
38574        24000          9          33677  
38575        18000          7          27679  

[38576 rows x 24 columns]>

df.dtypes

id                                int64
address_state                    object
application_type                 object
emp_length                       object
emp_title                        object
grade                            object
home_ownership                   object
issue_date               datetime64[ns]
last_credit_pull_date    datetime64[ns]
last_payment_date        datetime64[ns]
loan_status                      object
next_payment_date        datetime64[ns]
member_id                         int64
purpose                          object
sub_grade                        object
term                             object
verification_status              object
annual_income                   float64
dti                             float64
installment                     float64
int_rate                        float64
loan_amount                       int64
total_acc                         int64
total_payment                     int64
dtype: object

df.describe()

total_loan_applications = df['id'].count()
print("Total Loan Applications :", total_loan_applications)

Total Loan Applications : 38576

latest_issue_date = df['issue_date'].max()

# Step 3: Extract year and month
latest_year = latest_issue_date.year
latest_month = latest_issue_date.month

# Step 4: Filter data for that month
mtd_data = df[(df['issue_date'].dt.year == latest_year) &
              (df['issue_date'].dt.month == latest_month)]
# Step 5: Count applications
mtd_loan_applications = mtd_data['id'].count()
# Step 6: Print result
print(f"Month-to-Date Loan Applications (for {latest_issue_date.strftime('%B %Y')}): {mtd_loan_applications}")

Month-to-Date Loan Applications (for December 2021): 4314

total_funded_amount = df['loan_amount'].sum()
total_funded_amount_millions = total_funded_amount / 1000000
print("Total Funded Amount (in Millions): ${:.3f}M".format(total_funded_amount_millions))

Total Funded Amount (in Millions): $435.757M

latest_issue_date = df['issue_date'].max()
latest_year = latest_issue_date.year
latest_month = latest_issue_date.month

mtd_data = df[(df['issue_date'].dt.year == latest_year) &
              (df['issue_date'].dt.month == latest_month)]
mtd_total_funded_amount = mtd_data['loan_amount'].sum()
mtd_total_funded_amount_millions = mtd_total_funded_amount / 1_000_000

print("MTD Total Funded Amount ({}): ${:.2f}M".format(latest_issue_date.strftime('%B %Y'), mtd_total_funded_amount_millions))

MTD Total Funded Amount (December 2021): $53.98M

total_received = df['total_payment'].sum()
total_received_millions = total_received / 1_000_000
print("Total Amount Received (in Millions): ₹{:.2f}M".format(total_received_millions))

Total Amount Received (in Millions): ₹473.07M

latest_issue_date = df['issue_date'].max()
latest_year = latest_issue_date.year
latest_month = latest_issue_date.month

mtd_data = df[(df['issue_date'].dt.year == latest_year) &
             (df['issue_date'].dt.month == latest_month)]
mtd_total_received_amount = mtd_data['total_payment'].sum()
mtd_total_received_amount_millions = mtd_total_received_amount / 1_000_000
print("MTD Total Received Amount ({}): ${:.2f}M".format(latest_issue_date.strftime('%B %Y'), mtd_total_received_amount_millions))

MTD Total Received Amount (December 2021): $58.07M

avg_interest_rate = df['int_rate'].mean() * 100
print("Average Interest Rate: {:.2f}%".format(avg_interest_rate))

Average Interest Rate: 12.05%

avg_dti = df['dti'].mean() * 100
print("Average DTI: {:.2f}%".format(avg_dti))

Average DTI: 13.33%

# Define Good Loan conditions (Fully Paid or Current)
good_loans = df[df['loan_status'].isin(["Fully Paid", "Current"])]

# Total Loan Applications
total_loan_applications = df['id'].count()

# Good Loan Calculations
good_loan_applications = good_loans['id'].count()
good_loan_funded_amount = good_loans['loan_amount'].sum()
good_loan_received = good_loans['total_payment'].sum()

# Convert to Millions
good_loan_funded_amount_millions = good_loan_funded_amount / 1_000_000
good_loan_received_millions = good_loan_received / 1_000_000

# Percentage of Good Loans
good_loan_percentage = (good_loan_applications  / total_loan_applications) * 100

# Print Results
print("Good Loan Applications:", good_loan_applications)
print("Good Loan Funded Amount (in Millions): ${:.2f}M".format(good_loan_funded_amount_millions))
print("Good Loan Total Received (in Millions): ${:.2f}M".format(good_loan_received_millions))
print("Percentage of Good Loan Applications: {:.2f}%".format(good_loan_percentage))

Good Loan Applications: 33243
Good Loan Funded Amount (in Millions): $370.22M
Good Loan Total Received (in Millions): $435.79M
Percentage of Good Loan Applications: 86.18%

# Define Bad Loan conditions
bad_loans = df[df['loan_status'].isin(["Charged Off"])]

# Total Loan Applications (already known or recompute)
total_loan_applications = df['id'].count()

# Bad Loan Calculations
bad_loan_applications = bad_loans['id'].count()
bad_loan_funded_amount = bad_loans['loan_amount'].sum()
bad_loan_received = bad_loans['total_payment'].sum()

# Convert to Millions
bad_loan_funded_amount_millions = bad_loan_funded_amount / 1_000_000
bad_loan_received_millions = bad_loan_received / 1_000_000

# Percentage of bad Loans
bad_loan_percentage = (bad_loan_applications  / total_loan_applications) * 100

# Print Results
print("Bad Loan Applications:", bad_loan_applications)
print("Bad Loan Funded Amount (in Millions): ₹{:.2f}M".format(bad_loan_funded_amount_millions))
print("Bad Loan Total Received (in Millions): ₹{:.2f}M".format(bad_loan_received_millions))
print("Percentage of Bad Loan Applications: {:.2f}%".format(bad_loan_percentage))

Bad Loan Applications: 5333
Bad Loan Funded Amount (in Millions): ₹65.53M
Bad Loan Total Received (in Millions): ₹37.28M
Percentage of Bad Loan Applications: 13.82%

# Convert issue_date to datetime
df['issue_date'] = pd.to_datetime(df['issue_date'])
# Get the Latest month & year for MTD
latest_month = df['issue_date'].dt.month.max()
latest_year = df['issue_date'].dt.year.max()

# MTD Filter
mtd_df = df[(df['issue_date'].dt.month == latest_month) & (df['issue_date'].dt.year == latest_year)]

# Define aggregation functions
summary = df.groupby('loan_status').agg(
    Total_Loan_Applications = ('id', 'count'),
    Total_Funded_Amount = ('loan_amount', 'sum'),
    Total_Amount_Received = ('total_payment', 'sum'),
    Avg_Interest_Rate = ('int_rate', 'mean'),
    Avg_DTI = ('dti', 'mean')
)

# MTD Aggregations
mtd_summary = mtd_df.groupby('loan_status').agg(
    MTD_Funded_Amount = ('loan_amount', 'sum'),
    MTD_Amount_Received = ('total_payment', 'sum')
)

# Merge both summaries
final_summary = summary.merge(mtd_summary, on='loan_status', how = 'left')

# Convert currency columns to ₹ Millions
for col in ['Total_Funded_Amount', 'Total_Amount_Received', 'MTD_Funded_Amount', 'MTD_Amount_Received']:
    final_summary[col] = final_summary[col] / 1_000_000

# Format columns
final_summary = final_summary.round({
    'Total_Funded_Amount' : 2,
    'Total_Amount_Received' : 2,
    'MTD_Funded_Amount' : 2,
    'MTD_Amount_Received' : 2,
    'Avg_Interest_Rate' : 2,
    'Avg_DTI' : 2
})

# Display
print(final_summary.reset_index())

   loan_status  Total_Loan_Applications  Total_Funded_Amount  \
0  Charged Off                     5333                65.53   
1      Current                     1098                18.87   
2   Fully Paid                    32145               351.36   

   Total_Amount_Received  Avg_Interest_Rate  Avg_DTI  MTD_Funded_Amount  \
0                  37.28               0.14     0.14               8.73   
1                  24.20               0.15     0.15               3.95   
2                 411.59               0.12     0.13              41.30   

   MTD_Amount_Received  
0                 5.32  
1                 4.93  
2                47.82

monthly_funded = (
    df.sort_values('issue_date')
      .assign(month_name=lambda x: x['issue_date'].dt.strftime('%b %Y'))
      .groupby('month_name', sort=False)['loan_amount']
      .sum()
      .div(1_000_000)
      .reset_index(name='loan_amount_millions')
)

#plotting
plt.figure(figsize=(10, 5))
plt.fill_between(monthly_funded['month_name'], monthly_funded['loan_amount_millions'], color='skyblue', alpha=0.5)
plt.plot(monthly_funded['month_name'], monthly_funded['loan_amount_millions'], color='blue', linewidth=2)

# Data labels
for i, row in monthly_funded.iterrows():
    plt.text(i, row['loan_amount_millions'] + 0.1, f"{row['loan_amount_millions']:.2f}",
             ha = 'center', va = 'bottom', fontsize = 9, rotation = 0, color = 'black')

# Formatting
plt.title('Total Funded Amount by Month', fontsize=14)
plt.xlabel('Month')
plt.ylabel('Funded Amount (₹ Millions)')
plt.xticks(ticks=range(len(monthly_funded)), labels=monthly_funded['month_name'], rotation=45)
plt.grid(True, linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

# make sure 'issue_date' is datetime
df['issue_date'] = pd.to_datetime(df['issue_date'])

# Create 'Month Year' column for grouping
df['month_name'] = df['issue_date'].dt.strftime('%b %Y')

# Sort by date to keep month order intact
df = df.sort_values('issue_date')

# Group by month and calculate total received
monthly_received = df.groupby('month_name',sort=False)['total_payment'].sum().reset_index()

# Convert to ₹ millions
monthly_received['total_payment_millions'] = monthly_received['total_payment'] / 1_000_000

# Plotting the area chart
plt.figure(figsize=(10, 5))
plt.fill_between(monthly_received['month_name'], monthly_received['total_payment_millions'], color='lightgreen', alpha=0.5)
plt.plot(monthly_received['month_name'], monthly_received['total_payment_millions'], color='green', linewidth=2)

# Data labels
for i, row in monthly_received.iterrows():
    plt.text(row['month_name'], row['total_payment_millions']+ 0.1, f"{row['total_payment_millions']:.2f}",
             ha = 'center', va = 'bottom', fontsize = 9, color = 'black')

# Formatting
plt.title('Total Amount Received by Month', fontsize=14)
plt.xlabel('Month')
plt.ylabel('Amount Received (₹ Millions)')
plt.xticks(rotation=45)
plt.grid(True, linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

# MAke sure 'issue_date' is datetime
df['issue_date'] = pd.to_datetime(df['issue_date'])

# Create 'Month Year' column for grouping
df['month_name'] = df['issue_date'].dt.strftime('%b %Y')

# Sort issue date to maintain month order
df = df.sort_values('issue_date')

# Group by month & count Loan applications
monthly_applications = df.groupby('month_name', sort=False)['id'].count().reset_index()
monthly_applications.rename(columns={'id': 'total_applications'}, inplace=True)

# Plotting the area chart
plt.figure(figsize=(10, 5))
plt.fill_between(monthly_applications['month_name'], monthly_applications['total_applications'], color='yellow', alpha=0.5)
plt.plot(monthly_applications['month_name'], monthly_applications['total_applications'], color='blue', linewidth=2)

# Data labels
for i, row in monthly_applications.iterrows():
    plt.text(row['month_name'], row['total_applications']+ 5, f"{row['total_applications']}",
             ha = 'center', va = 'bottom', fontsize = 9, color = 'black')

# Formatting
plt.title('Total Loan Applications by Month', fontsize=14)
plt.xlabel('Month')
plt.ylabel('Number of Applications')
plt.xticks(rotation=45)
plt.grid(True, linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

# Group by state & sum Loan amounts (in thousands)
state_funding = df.groupby('address_state')['loan_amount'].sum().sort_values(ascending=True)
state_funding_thousands = state_funding / 1000 # convert to ₹ '000

# Plot horizontal bar chart
plt.figure(figsize=(10,8))
bars = plt.barh(state_funding_thousands.index, state_funding_thousands.values, color='lightcoral')

# Add data Labels with 'K'
for bar in bars:
    width = bar.get_width()
    plt.text(width + 10, bar.get_y() + bar.get_height() / 2, f'{width:,.0f}K', va = 'center', fontsize=9)

plt.title('Total Funded Amount by State', fontsize=14)
plt.xlabel('Funded Amount (₹ \'000)')
plt.ylabel('State')
plt.tight_layout()
plt.show()

# Group total funded amount by term
term_funding = df.groupby('term')['loan_amount'].sum()

# Convert to ₹ Millions
term_funding_millions = term_funding / 1_000_000

# Function to display both percent & amount
def autopct_format(pct, allvals):
    absolute = int(round(pct/100.*sum(allvals)))
    amount = allvals[absolute == allvals].value[0] if absolute in allvals.values else absolute
    return f"{pct:.1f}%\n₹{amount:.1f}M"

# Plotting the donut chart
plt.figure(figsize=(10,8))
colors = ['#66c2a5', '#fc8d62'] # Customize if needed

wedges, texts, autotexts = plt.pie(
    term_funding_millions,
    labels=term_funding_millions.index,
    autopct=lambda pct: autopct_format(pct, term_funding_millions),
    startangle=90,
    colors=colors,
    wedgeprops={'width': 0.4}
)
# Draw center circle
plt.gca().add_artist(plt.Circle((0,0), 0.70, color='white'))
plt.title("Total Funded Amount by Term (in ₹ Millions)")
plt.tight_layout()
plt.show()

# Group total funded amount by emp_Length
emp_funding = df.groupby('emp_length')['loan_amount'].sum().sort_values()

# Convert to ₹ Thousands
emp_funding_thousands = emp_funding / 1_000

# Plotting horizontal bar chart
plt.figure(figsize=(10, 6))
bars = plt.barh(emp_funding_thousands.index, emp_funding_thousands, color='purple')

# data labels
for bar in bars:
    width = bar.get_width()
    plt.text(width + 5, bar.get_y() + bar.get_height() / 2, f"₹{width:,.0f}K", va='center',fontsize=9)

# chart formatting
plt.xlabel('Funded Amount (₹ Thousands)')
plt.title('Total Funded Amount by Employment Length')
plt.grid(axis = 'x', linestyle='--', alpha = 0.5)
plt.tight_layout()
plt.show()

# Group by purpose and calculate total Loan amount
purpose_funding = df.groupby('purpose')['loan_amount'].sum().reset_index()
purpose_funding = purpose_funding.sort_values(by='loan_amount', ascending=True) # Ascending for horizontal bar chart

# Convert to ₹ millions
purpose_funding['loan_amount_millions'] = purpose_funding['loan_amount'] / 1_000_000

# Plot
plt.figure(figsize=(10, 6))
bars = plt.barh(purpose_funding['purpose'], purpose_funding['loan_amount_millions'], color='skyblue')

# data labels
for bar in bars:
    width = bar.get_width()
    plt.text(width + 1, bar.get_y() + bar.get_height() / 2, f"₹{width:,.0f}K", va='center',fontsize=9)

# chart formatting
plt.title('Total Funded Amount by Loan Purpose (₹ Millions)', fontsize=14)
plt.xlabel('Funded Amount (₹ Millions)')
plt.ylabel('Loan Purpose')
plt.grid(axis = 'x', linestyle='--', alpha = 0.6)
plt.tight_layout()
plt.show()

# Group by home_ownership and calculate total funded amount
home_funding = df.groupby('home_ownership')['loan_amount'].sum().reset_index()
home_funding['loan_amount_millions'] = home_funding['loan_amount'] / 1_000_000

# Create Tree Map
fig = px.treemap(
    home_funding,
    path = ['home_ownership'],
    values = 'loan_amount_millions',
    color = 'loan_amount_millions',
    color_continuous_scale = 'Blues',
    title = 'Total Funded Amount by Home Ownership (₹ Millions)'
)
# Show chart
fig.show()

	id	issue_date	last_credit_pull_date	last_payment_date	next_payment_date	member_id	annual_income	dti	installment	int_rate	loan_amount	total_acc	total_payment
count	3.857600e+04	38576	38576	38576	38576	3.857600e+04	3.857600e+04	38576.000000	38576.000000	38576.000000	38576.000000	38576.000000	38576.000000
mean	6.810371e+05	2021-07-16 02:31:35.562007040	2021-06-08 13:36:34.193280512	2021-06-26 09:52:08.909166080	2021-07-26 20:42:20.605557760	8.476515e+05	6.964454e+04	0.133274	326.862965	0.120488	11296.066855	22.132544	12263.348533
min	5.473400e+04	2021-01-01 00:00:00	2021-01-08 00:00:00	2021-01-08 00:00:00	2021-02-08 00:00:00	7.069900e+04	4.000000e+03	0.000000	15.690000	0.054200	500.000000	2.000000	34.000000
25%	5.135170e+05	2021-04-11 00:00:00	2021-04-15 00:00:00	2021-03-16 00:00:00	2021-04-16 00:00:00	6.629788e+05	4.150000e+04	0.082100	168.450000	0.093200	5500.000000	14.000000	5633.000000
50%	6.627280e+05	2021-07-11 00:00:00	2021-05-16 00:00:00	2021-06-14 00:00:00	2021-07-14 00:00:00	8.473565e+05	6.000000e+04	0.134200	283.045000	0.118600	10000.000000	20.000000	10042.000000
75%	8.365060e+05	2021-10-11 00:00:00	2021-08-13 00:00:00	2021-09-15 00:00:00	2021-10-15 00:00:00	1.045652e+06	8.320050e+04	0.185900	434.442500	0.145900	15000.000000	29.000000	16658.000000
max	1.077501e+06	2021-12-12 00:00:00	2022-01-20 00:00:00	2021-12-15 00:00:00	2022-01-15 00:00:00	1.314167e+06	6.000000e+06	0.299900	1305.190000	0.245900	35000.000000	90.000000	58564.000000
std	2.113246e+05	NaN	NaN	NaN	NaN	2.668105e+05	6.429368e+04	0.066662	209.092000	0.037164	7460.746022	11.392282	9051.104777

BANK LOAN ANALYSIS REPORT¶

Import libraries¶

Metadata of data¶

Data types¶

Primary KPI's¶

Month to Date (MTD) - Total Loan Applications¶

Total Funded Amount (Millions)¶

Month to Date (MTD) - Total Funded Amount (Millions)¶

Total Amount Received (Millions)¶

Month to Date (MTD) - Total Amount Received (Millions)¶

Average Interest Rate¶

Average Debt-to-Income Ratio (DTI)¶

Secondary KPI's¶

Good Loan Metrics¶

Bad Loan Metrics¶

Loan Status¶

Total Funded Amount by Month¶

Total Received Amount by Month¶

Total Applications by Month¶

Total Funded Amount by State¶

Total Funded Amount by Team¶

Total Funded Amount by Employee Length¶

Total Funded Amount by Purpose¶

Total Funded Amount by Home Ownership¶

	id	address_state	application_type	emp_length	emp_title	grade	home_ownership	issue_date	last_credit_pull_date	last_payment_date	...	sub_grade	term	verification_status	annual_income	dti	installment	int_rate	loan_amount	total_acc	total_payment
0	1077430	GA	INDIVIDUAL	< 1 year	Ryder	C	RENT	2021-02-11	2021-09-13	2021-04-13	...	C4	60 months	Source Verified	30000.0	0.0100	59.83	0.1527	2500	4	1009
1	1072053	CA	INDIVIDUAL	9 years	MKC Accounting	E	RENT	2021-01-01	2021-12-14	2021-01-15	...	E1	36 months	Source Verified	48000.0	0.0535	109.43	0.1864	3000	4	3939
2	1069243	CA	INDIVIDUAL	4 years	Chemat Technology Inc	C	RENT	2021-01-05	2021-12-12	2021-01-09	...	C5	36 months	Not Verified	50000.0	0.2088	421.65	0.1596	12000	11	3522
3	1041756	TX	INDIVIDUAL	< 1 year	barnes distribution	B	MORTGAGE	2021-02-25	2021-12-12	2021-03-12	...	B2	60 months	Source Verified	42000.0	0.0540	97.06	0.1065	4500	9	4911
4	1068350	IL	INDIVIDUAL	10+ years	J&J Steel Inc	A	MORTGAGE	2021-01-01	2021-12-14	2021-01-15	...	A1	36 months	Verified	83000.0	0.0231	106.53	0.0603	3500	28	3835

	id	address_state	application_type	emp_length	emp_title	grade	home_ownership	issue_date	last_credit_pull_date	last_payment_date	...	sub_grade	term	verification_status	annual_income	dti	installment	int_rate	loan_amount	total_acc	total_payment
38571	803452	NJ	INDIVIDUAL	< 1 year	Joseph M Sanzari Company	C	MORTGAGE	2021-07-11	2021-05-16	2021-05-16	...	C1	60 months	Verified	100000.0	0.1986	551.64	0.1299	24250	33	31946
38572	970377	NY	INDIVIDUAL	8 years	Swat Fame	C	RENT	2021-10-11	2021-04-16	2021-05-16	...	C1	60 months	Verified	50000.0	0.0458	579.72	0.1349	25200	18	31870
38573	875376	CA	INDIVIDUAL	5 years	Anaheim Regional Medical Center	D	RENT	2021-09-11	2021-05-16	2021-05-16	...	D5	60 months	Verified	65000.0	0.1734	627.93	0.1749	25000	20	35721
38574	972997	NY	INDIVIDUAL	5 years	Brooklyn Radiology	D	RENT	2021-10-11	2021-05-16	2021-05-16	...	D5	60 months	Verified	368000.0	0.0009	612.72	0.1825	24000	9	33677
38575	682952	NY	INDIVIDUAL	4 years	Allen Edmonds	F	RENT	2021-07-11	2021-05-16	2021-05-16	...	F3	60 months	Verified	80000.0	0.0600	486.86	0.2099	18000	7	27679